IEEE-519-based real-time and optimal control of active filters under nonsinusoidal line voltages using neural networks

2002 ◽  
Vol 17 (3) ◽  
pp. 815-821 ◽  
Author(s):  
S.M.R. Rafiei ◽  
R. Ghazi ◽  
H.A. Toliyat
Keyword(s):  
Neural Networks ◽  
Optimal Control ◽  
Real Time ◽  
Active Filters

scholarly journals Real-Time Optimal Approach and Capture of ENVISAT Based on Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Hongjue Li ◽  
Yunfeng Dong ◽  
Peiyun Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimal Control ◽  
Real Time ◽  
Rbf Neural Networks ◽  
Optimal Control Strategy ◽  
Neural Network Controller ◽  
Training Samples ◽  
Time Optimal ◽  
Optimal Approach

A neural network-based controller is developed to enable a chaser spacecraft to approach and capture a disabled Environmental Satellite (ENVISAT). This task is conventionally tackled by framing it as an optimal control problem. However, the optimization of such a problem is computationally expensive and not suitable for onboard implementation. In this work, a learning-based approach is used to rapidly generate the control outputs of the controller based on a series of training samples. These training samples are generated by solving multiple optimal control problems with successive iterations. Then, Radial Basis Function (RBF) neural networks are designed to mimic this optimal control strategy from the generated data. Compared with a traditional controller, the neural network controller is able to generate real-time high-quality control policies by simply passing the input through the feedforward neural network.

scholarly journals Real-time optimal control for irregular asteroid landings using deep neural networks

2020 ◽  
Vol 170 ◽  
pp. 66-79 ◽  
Author(s):  
Lin Cheng ◽  
Zhenbo Wang ◽  
Yu Song ◽  
Fanghua Jiang
Keyword(s):  
Neural Networks ◽  
Optimal Control ◽  
Real Time ◽  
Deep Neural Networks ◽  
Time Optimal Control ◽  
Time Optimal
Sign in / Sign up

Export Citation Format

Share Document